


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()

class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						PriorTransformer
    __magic_name__         =						'hidden_states'
    @property
    def 						a_     (			self       ):
         snake_case											=							4
         snake_case											=							8
         snake_case											=							7

         snake_case											=							floats_tensor((batch_size, embedding_dim)       ).to(__snake_case       )

         snake_case											=							floats_tensor((batch_size, embedding_dim)       ).to(__snake_case       )
         snake_case											=							floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(__snake_case       )

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }
    def 						a_     (			self     ,   __snake_case=0       ):
         torch.manual_seed(__snake_case       )
         snake_case											=							4
         snake_case											=							8
         snake_case											=							7

         snake_case											=							torch.randn((batch_size, embedding_dim)       ).to(__snake_case       )

         snake_case											=							torch.randn((batch_size, embedding_dim)       ).to(__snake_case       )
         snake_case											=							torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(__snake_case       )

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }
    @property
    def 						a_     (			self       ):
         return (4, 8)
    @property
    def 						a_     (			self       ):
         return (4, 8)
    def 						a_     (			self       ):
         snake_case											=							{
             '''num_attention_heads''': 2,
             '''attention_head_dim''': 4,
             '''num_layers''': 2,
             '''embedding_dim''': 8,
             '''num_embeddings''': 7,
             '''additional_embeddings''': 4,
         }
         snake_case											=							self.dummy_input
         return init_dict, inputs_dict
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							PriorTransformer.from_pretrained(
             '''hf-internal-testing/prior-dummy'''     ,   output_loading_info=__snake_case       )
         self.assertIsNotNone(__snake_case       )
         self.assertEqual(len(loading_info['''missing_keys''']       )     ,   0       )

         model.to(__snake_case       )
         snake_case											=							model(**self.dummy_input       )[0]

         assert hidden_states is not None, "Make sure output is not None"
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.prepare_init_args_and_inputs_for_common()

         snake_case											=							self.model_class(**__snake_case       )
         snake_case											=							inspect.signature(model.forward       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case											=							[*signature.parameters.keys()]

         snake_case											=							['''hidden_states''', '''timestep''']
         self.assertListEqual(arg_names[:2]     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''       )
         snake_case											=							model.to(__snake_case       )

         if hasattr(__snake_case     ,   '''set_default_attn_processor'''       ):
              model.set_default_attn_processor()

         snake_case											=							self.get_dummy_seed_input()

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )[0]

         snake_case											=							output[0, :5].flatten().cpu()
         print(__snake_case       )

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         snake_case											=							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]       )
         self.assertTrue(torch_all_close(__snake_case     ,   __snake_case     ,   rtol=1E-2       )       )






@slow
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self     ,   __snake_case=1     ,   __snake_case=7_6_8     ,   __snake_case=7_7     ,   __snake_case=0       ):
         torch.manual_seed(__snake_case       )
         snake_case											=							batch_size
         snake_case											=							embedding_dim
         snake_case											=							num_embeddings

         snake_case											=							torch.randn((batch_size, embedding_dim)       ).to(__snake_case       )

         snake_case											=							torch.randn((batch_size, embedding_dim)       ).to(__snake_case       )
         snake_case											=							torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(__snake_case       )

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }
    def 						a_     (			self       ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




    @parameterized.expand(
        [
            # fmt: off
            [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
            [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
            # fmt: on
        ]       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''     ,   subfolder='''prior'''       )
         model.to(__snake_case       )
         snake_case											=							self.get_dummy_seed_input(seed=__snake_case       )

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )[0]

         assert list(sample.shape       ) == [1, 7_6_8]

         snake_case											=							sample[0, :8].flatten().cpu()
         print(__snake_case       )
         snake_case											=							torch.tensor(__snake_case       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=1E-3       )








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




